Visualisation of Data Envelopment Analysis in primary health services

Area of Science:

• Public Health
• Health Services Research
• Econometrics

Background:

• Traditional efficiency analysis in public health often overlooks primary care providers.
• Data Envelopment Analysis (DEA) is a common method for assessing decision-making unit (DMU) efficiency but has limitations in differentiating units and handling outliers.
• Existing DEA enhancements like super-efficiency and cross-efficiency may exclude extreme cases and ignore specialization.

Purpose of the Study:

• To introduce and apply DEA Visualisation for analyzing efficiency in public primary health centers.
• To identify sources of inefficiency and patterns of specialization among centers serving senior citizens.
• To provide insights for improving resource allocation and cost-effectiveness in primary healthcare.

Main Methods:

• Utilized DEA Visualisation, integrating DEA with multivariate statistical methods.
• Analyzed data from 82 public primary health centers in Madrid serving senior citizens in 2018.
• Identified inefficiency sources, specialization patterns, and extreme cases without removing outliers.

Main Results:

• Identified inefficiencies, including a preference for specific over generic drug prescriptions, contributing to increased costs.
• Detected two outlier centers with high infrastructure costs and disproportionate staffing.
• Found that centers emphasizing preventive care were more cost-effective, particularly in reducing prescription expenses.

Conclusions:

• DEA Visualisation offers a robust method for identifying inefficiencies and specializations in healthcare settings.
• Strategies such as optimizing drug prescribing and redistributing patients can enhance primary care efficiency.
• Focusing on preventive care models can lead to significant cost savings, especially in pharmaceutical spending.